loading page

Prediction of synoptic-scale sea level pressure over the Indian monsoon region using deep learning
  • +2
  • Aryaman Sinha ,
  • Mayuna Gupta ,
  • K S S Sai Srujan ,
  • Hariprasad Kodamana ,
  • Sandeep Sukumaran
Aryaman Sinha
Author Profile
Mayuna Gupta
Author Profile
K S S Sai Srujan
Author Profile
Hariprasad Kodamana
IIT Delhi

Corresponding Author:[email protected]

Author Profile
Sandeep Sukumaran
Author Profile


The synoptic-scale (3 - 7 days) variability is a dominant contributor to the Indian summer monsoon (ISM) seasonal precipitation. An accurate prediction of ISM precipitation by dynamical or statistical models remains a challenge. Here we show that the sea level pressure (SLP) can be used as a proxy to predict the active-break cycle as well as the genesis of low- pressure-systems (LPS), using a deep learning model, namely, convolutional long short-term memory (ConvLSTM) networks. The deep learning model is able to reliably predict the daily SLP anomalies over Central India and the Bay of Bengal at a lead time of 7 days. As the fluctuations in SLP drive the changes in the strength of the atmospheric circulation, the prediction of SLP anomalies is useful in predicting the intensity of ISM. It is demonstrated that the ConvLSTM possesses better prediction skill compared to a conventional numerical weather prediction model, indicating the usefulness of a physics guided deep learning model in medium range weather forecasting.
2022Published in IEEE Geoscience and Remote Sensing Letters volume 19 on pages 1-5. 10.1109/LGRS.2021.3100899